The taper of cast post preparation measured using innovative image processing technique.

BACKGROUND: No documentation in the literature about taper of cast posts. This study was conducted to measure the degree of cast posts taper, and to evaluate its suitability based on the anatomy aspects of the common candidate teeth for post reconstruction. METHODS: Working casts for cast posts, prepared using Gates Glidden drills, were collected. Impressions of post spaces were made using polyvinyl siloxan putty/wash technique. Digital camera with a 10' high quality lens was used for capturing two digital images for each impression; one in the Facio-Lingual (FL) and the other in the Mesio-Distal (MD) directions. Automated image processing program was developed to measure the degree of canal taper. Data were analyzed using Statistical Package for Social Sciences software and One way Analysis of Variance. RESULTS: Eighty four dies for cast posts were collected: 16 for each maxillary anterior teeth subgroup, and 18 for each maxillary and mandibular premolar subgroup. Mean of total taper for all preparations was 10.7 degree. There were no statistical differences among the total taper of all groups (P = .256) or between the MD and FL taper for each subgroup. Mean FL taper for the maxillary first premolars was lower significantly (P = .003) than the maxillary FL taper of the second premolars. FL taper was higher than the MD taper in all teeth except the maxillary first premolars. CONCLUSIONS: Taper produced did not reflect the differences among the anatomy of teeth. While this technique deemed satisfactory in the maxillary anterior teeth, the same could not be said for the maxillary first premolars. Careful attention to the root anatomy is mandatory.

A novel approach for quantitative assessment of mucosal damage in inflammatory bowel disease.

AIMS: One of the main reliable histological features to suggest the diagnosis of inflammatory bowel disease is the presence of significant distortion of the crypt architecture indicating the chronic nature of the disease resulting in mucosal damage. This feature has a considerable intra-observer and inter-observer variability leading to significant subjectivity in colonic biopsy assessment. In this paper, we present a novel automated system to assess mucosal damage and architectural distortion in inflammatory bowel disease (IBD). METHODS: The proposed system relies on advanced image understating and processing techniques to segment digitally acquired images of microscopic biopsies, then, to extract key features to quantify the crypts irregularities in shape and distribution. These features were used as inputs to an artificial intelligent classifier that, after a training phase, can carry out the assessment automatically. RESULTS: The developed system was evaluated using 118 IBD biopsies. 116 out of 118 biopsies were correctly classified as compared to the consensus of three expert pathologists, achieving an overall precision of 98.31%. CONCLUSIONS: An automated intelligent system to quantitatively assess inflammatory bowel disease was developed. The proposed system utilized advanced image understanding techniques together with an intelligent classifier to conduct the assessment. The developed system proved to be reliable, robust, and minimizes subjectivity and inter- and intra-observer variability. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1797721309305023.

An intelligent decision support system for quantitative assessment of gastric atrophy.

AIMS: To build an automated decision support system to assist pathologists in grading gastric atrophy according to the updated Sydney system. METHODS: A database of 143 biopsies was used to train and examine the proposed system. A panel of three experienced pathologists reached a consensus regarding the grading of the studied biopsies using the visual scale of the updated Sydney system. Digital imaging techniques were utilised to extract a set of discriminating morphological features that describe each atrophy grade sufficiently and uniquely. A probabilistic neural networks structure was used to build a grading system. To evaluate the performance of the proposed system, 66% of the biopsies (94 biopsy images) were used for training purposes and 34% (49 biopsy images) were used for testing and validation purposes. RESULTS: During the training phase, a 98.9% precision was achieved, whereas during testing, a precision of 95.9% was achieved. The overall precision achieved was 97.9%. CONCLUSIONS: A fully automated decision support system to grade gastric atrophy according to the updated Sydney system is proposed. The system utilises advanced image processing techniques and probabilistic neural networks in conducting the assessment. The proposed system eliminates inter- and intra-observer variations with high reproducibility.